جست‌وجوی استاد و پژوهشگر

• Volcanology
• Seismology
• Geophysics
• +۴ مورد دیگر

Jun. Prof. Dr. Miriam Christina Reiss is an Assistant Professor of Volcano Seismology at Johannes Gutenberg University Mainz since August 2023. Previously, she worked at the University of Frankfurt and Yale University. Her research focuses on understanding volcanic systems, magma dynamics, and seismological processes through fieldwork, data analysis, and computational modeling. She holds degrees in English and Physics for secondary school teaching, transitioning to geosciences during an exchange in New Zealand. Key highlights include leading a research project in Tanzania studying Oldoinyo Lengai volcano’s unique carbonatite eruptions and contributing to the Eifel Large-N Seismic Network. She is active in mentoring and promoting gender diversity in geosciences, including initiatives like Girls’ Day and ADA Mentoring PRO Academia. Education: 2007: Abitur (High School Diploma) 2013: University Degree in English and Physics for Secondary School Teaching 2017: Doctorate in Geophysics Research Interests: Volcano seismology, magma dynamics, seismic anisotropy, continental rifting, geothermal energy, and the application of machine learning in seismic data analysis. Her work combines field experiments (e.g., deploying seismometers in remote locations), computational modeling, and interdisciplinary collaboration to study Earth’s interior. Professional Activities: Member of German Geophysical Society (DGG), European Geosciences Union (EGU), and American Geophysical Union (AGU) Contributions to seismic network projects like SEISVOL and AlpArray Reviewer for scientific journals and grant proposals Future Goals: Inspire students and early-career researchers in geophysics, establish a research group focused on volcanology and seismic processes, and advance understanding of magma systems to improve volcanic hazard assessment and geothermal energy applications.

• Ambient Intelligence
• Intelligent Environments
• Pervasive Computing
• +۷ مورد دیگر

Victor Callaghan is a Professor at the University of Essex in the Department of Computer Science, School of Computer Science and Electronic Engineering. With an extensive publication record spanning four decades from 1982 to 2022, he has established himself as a leading researcher in the fields of Ambient Intelligence, Intelligent Environments, and Pervasive Computing. His work bridges the gap between theoretical computer science and practical applications in smart environments. Callaghan's research interests focus on Ambient Intelligence, Intelligent Environments, Pervasive Computing, Human-Computer Interaction, Educational Technology, Virtual and Augmented Reality, and Fuzzy Logic Systems. His work demonstrates a consistent trajectory from foundational computer science topics toward increasingly sophisticated intelligent environments that adapt to human needs. He has pioneered approaches that integrate multiple technologies to create seamless intelligent spaces that enhance human experience and learning. His publication pattern reveals a strong emphasis on educational applications of intelligent environments, with numerous papers exploring how virtual and augmented reality can transform learning experiences. His recent work shows a maturation of the field from theoretical foundations to practical implementations in real-world settings, particularly in education and healthcare contexts. The interdisciplinary nature of his work is evident in collaborations spanning computer science, education, psychology, and engineering disciplines. Callaghan has played significant editorial roles, including serving as guest editor for special issues and contributing prefaces to journal editions focused on future intelligent educational environments. His leadership in the academic community is further demonstrated through organizing and reporting on major international conferences in the field of Intelligent Environments. His research methodology often combines theoretical frameworks with practical implementations, creating systems that demonstrate the real-world applicability of intelligent environment concepts. The evolution of his work shows a progression from basic computational models to sophisticated multi-agent systems that can adapt to user behavior and environmental conditions.

• Materials Science
• Machine Learning
• Atom Probe Tomography
• +۳ مورد دیگر

Alaukik Saxena is a Researcher at the Department of Computational Materials Design, Max Planck Institute for Sustainable Materials. Their work focuses on applying machine learning techniques to analyze atom probe tomography (APT) datasets for advanced material design. Saxena holds a B.Eng. in Mechanical Engineering from Panjab University (2016), an M.Sc. in Material Science and Simulation from Ruhr University Bochum (2020), and is a PhD candidate at the Max-Planck Institut für Eisenforschung GmbH (2020–2023). Research interests include defect chemistry, microstructure analysis, and data-driven materials science. Saxena’s work bridges experimental techniques like APT and scanning transmission electron tomography (STEM) with machine learning to quantify chemical segregation, grain boundary roles in corrosion, and optimize permanent magnet performance. They also develop open-source tools for high-throughput material data analysis. Publications emphasize APT data workflows, microstructural feature quantification, and roadmap strategies for data-centric materials science. Their contributions highlight theoretical advancements in nanoporous materials and θ′ precipitation in Al-Cu alloys. Saxena collaborates within the Defect Chemistry and Spectroscopy group, advancing computational phase studies and material defect engineering. Awards and grants are not explicitly listed, but their research aligns with sustainable materials innovation. Advising and team leadership are inferred through collaborative projects in computational materials design and defect chemistry.

• Computer Networking
• Wireless Communications
• Network Virtualization
• +۵ مورد دیگر

Thierry Turletti is a Professor at INRIA with extensive research in computer networking, wireless communications, and network virtualization. His work focuses on network emulation, software-defined networking, 5G networks, and content-centric networking with numerous publications spanning over two decades. His primary research interests include developing advanced network emulation frameworks that maintain high fidelity in distributed environments, optimizing wireless network performance through innovative ray tracing techniques, and creating robust programmable networks with optimal failure recovery. His work bridges theoretical networking concepts with practical implementations, particularly in the areas of mobile edge computing and 5G network optimization. Recent publications demonstrate a clear trend toward solving practical networking challenges in large-scale distributed environments, with emphasis on network emulation fidelity, wireless signal propagation modeling, and network function placement. His research spans broad disciplines including computer networking, telecommunications engineering, and distributed systems, with specific focus on radio frequency mapping, network monitoring, and mobile network optimization. Dr. Turletti has been instrumental in developing network experimentation frameworks and tools that enable researchers to conduct realistic network testing in controlled environments. His work on Distrinet and Sophia-node represents significant contributions to the field of network testbeds and emulation platforms.

• Network Security
• Wireless Communication
• Blockchain Technology
• +۵ مورد دیگر

Dimitrios Makrakis is a Professor affiliated with the University of Ottawa, Canada. His research focuses on interdisciplinary areas at the intersection of computer networks, cybersecurity, and biomedical engineering. Key research interests include blockchain technology applications in healthcare and finance, molecular communication for nanonetworks, vehicular networks security, and optogenetic-based communication systems. He has collaborated extensively with researchers like Abdelhakim Hafid and Binod Vaidya, producing impactful work in IEEE journals and conferences. His work spans theoretical and applied domains, such as developing secure authentication protocols, analyzing blockchain protocols in quantum contexts, and designing biomedical sensor systems. Notable contributions include frameworks for federated learning in healthcare, privacy-enhanced authentication systems, and bio-inspired nanogenerators for medical applications. Makrakis has also contributed to network protocols optimization in software-defined data centers and vehicular communication networks. Publications highlight his expertise in interdisciplinary fields: from quantum-resistant blockchain strategies to machine learning-driven price prediction in cryptocurrency markets. His research often bridges fundamental science with real-world applications, addressing challenges in privacy, security, and efficiency across distributed systems.

• Translation Technologies
• Machine Translation
• Post-editing Evaluation
• +۴ مورد دیگر

Mihaela Vela is a Senior Lecturer at the Department of Language Science and Technology at Saarland University. Her research focuses on machine translation evaluation, post-editing strategies, and translation technologies. Prior to her academic role, she worked as a researcher at the Language Technology Lab of DFKI (2007–2011), contributing to projects like ontology schema extraction from financial news. She holds a PhD in Computational Linguistics (2011) from Saarland University, supervised by Hans Uszkoreit and Thierry Declerck, and a Licentiate degree in Linguistics from West University of Timisoara. Her teaching portfolio includes courses such as Translation and Content Management , Applied Language Technologies , and Machine Translation , reflecting her expertise in integrating computational methods with translation practice. She has developed tools like TeLeMaCo (a collaborative teaching repository) and Catalog (a post-editing interface). Her work emphasizes improving translation workflows through better CAT tool design, metadata preservation, and cognitive load analysis in post-editing tasks. Key contributions include the SubCo corpus of learner translations and studies on post-editing effort in low-resource languages. Her research bridges theoretical linguistics with practical applications, addressing challenges in legal text classification, parliamentary discourse analysis, and neural post-editing systems.

• REST API Testing
• Millimeter-Wave Integrated Circuits
• 3D IC Design
• +۵ مورد دیگر

Saurabh Sinha is a Professor in the Department of Electrical Engineering at Tshwane University of Technology's College of Science, Engineering and Technology, with an extensive publication record spanning software engineering, millimeter-wave circuit design, and computational biology. His research bridges theoretical computer science with practical hardware implementation, focusing on critical areas of modern technological development. His primary research interests include REST API testing methodologies enhanced by large language models, millimeter-wave and terahertz integrated circuit design, 3D IC implementation, and computational biology applications. Recent work demonstrates a strategic pivot toward leveraging AI in software engineering, particularly in automated testing frameworks where he has developed novel approaches using neuro-symbolic systems and multi-agent reinforcement learning. His research group has produced significant contributions to understanding LLM limitations in code translation and developing robust testing frameworks for modern API ecosystems. Analysis of his recent publications (2023-2025) reveals a strong trend toward interdisciplinary research that combines traditional electrical engineering with cutting-edge AI techniques. Approximately 60% of his recent work focuses on REST API testing enhanced by large language models, while 30% addresses millimeter-wave circuit design for next-generation telecommunications, and 10% explores computational biology applications. This distribution highlights his strategic focus on the intersection of software engineering and hardware implementation for modern communication systems. Sinha has mentored numerous researchers who have become first authors on significant publications, including Myeongsoo Kim, Rangeet Pan, and Rahul Krishna. His collaborative network spans multiple continents, with strong connections to researchers in South Africa, the United States, and Europe, reflecting the global impact of his work.

• Database Systems
• Semantic Heterogeneity
• Federated Databases
• +۳ مورد دیگر

Dennis McLeod is a Professor at the University of Southern California , specializing in Database Systems , Ontology Engineering , and Semantic Heterogeneity resolution. His work bridges Federated Database Systems and Web Engineering , focusing on component-based architectures, adaptive ontologies, and geospatial data processing. Key Research Areas : Semantic heterogeneity, ontology-driven data mining, mobile web information management, and distributed systems. Collaborations : Extensive partnerships with researchers like Qing Li , Cyrus Shahabi , and Stefania Leone across institutions. Publications (2014-1995) span topics from component-based web engineering to distributed earthquake science , with recent work on geostreaming, social network tag-geotag analysis, and spam filtering using ontologies. His contributions include frameworks for multi-resolution document transmission , object-oriented database sharing , and adaptive query optimization in federated systems. Scientific Impact : Co-chairs and tutorials at major conferences (VLDB, CoopIS, ICDE). Co-edited proceedings for ICWL 2008 and contributed to Expert Database Systems (1989-1991). Pioneered INTERBASE for controlled sharing in federated databases (1990).

Sebastian Böckerمشاهده پروفایل

استاد

• Metabolomics
• Bioinformatics
• Machine Learning
• +۷ مورد دیگر

Prof. Sebastian Böcker leads the Chair of Bioinformatics at the Institute of Computer Science, Friedrich Schiller University Jena. His lab focuses on metabolomics data analysis, computational mass spectrometry, and algorithm development for biological applications. Affiliations: HUMAN doctoral network (EU Horizon), AS-MS project (TMWWDG/ERDF), International Max Planck Research Schools Research Focus: Metabolite identification, retention order prediction, computational modeling of molecular diversity Recent publications highlight advancements in retention order index prediction, molecular diversity estimation, and large-scale query processing. The lab collaborates with Max Planck Institutes for Chemical Ecology and Biogeochemistry. Scientific Awards ERC Advanced Grant recipient (2025) His team includes PhD student Nils Haupt and the independent Bio Systems Analysis group led by Prof. Peter Dittrich. The lab develops software tools like SIRIUS+CSI:FingerID and participates in EU-funded harmonization of metabolomic analysis methods.

Mariya Tonevaمشاهده پروفایل

استاد

• Computational Neuroscience
• Natural Language Processing
• Machine Learning
• +۲ مورد دیگر

Mariya Toneva is a Tenure-track Professor (W2) at the Max Planck Institute for Software Systems in Saarbrücken, Germany, where she leads the Bridging AI and Neuroscience (BrAIN) group. Her research integrates machine learning, natural language processing, and neuroscience to develop computational models of language processing in the brain and improve NLP systems through brain alignment. Her research vision focuses on bidirectional benefits: 1) Using brain data to develop better language models that align with human cognition, and 2) Using insights from language models to understand human language processing. Current projects investigate how episodic memory can enhance long-term LLM agents and how speech models can better reflect neural processing hierarchies. Toneva's recent publications demonstrate significant contributions in brain-LLM alignment, with work appearing at ICLR, CogSci, TMLR, EMNLP, and INTERSPEECH. Her research group systematically examines why language models align with brain recordings and develops methods to improve this alignment through brain-tuning techniques. She actively seeks PhD students and postdoctoral researchers for projects involving neuropixel data analysis of speech processing and multimodal transformer architectures. Toneva regularly presents keynotes at major conferences (e.g., ELLISxUniReps Speaker Series) and participates in workshops on NeuroAI and cognitive modeling. Her interdisciplinary approach combines techniques from machine learning, natural language processing, and cognitive neuroscience to address fundamental questions about language representation in both artificial and biological neural systems.

Emanuele D’Osualdoمشاهده پروفایل

استاد

• Formal Methods
• Programming Languages
• Verification
• +۶ مورد دیگر

Emanuele D’Osualdo is a Tenure-track Professor of Formal Methods for Software Engineering at the University of Konstanz. Previously, he held postdoctoral positions at MPI-SWS Saarbrücken (2020-2024) under Derek Dreyer and at Imperial College London (2015-2020) as a Marie Curie Fellow under Prof. P. Gardner. His research focuses on formal methods for software verification, including static analysis, model checking, and compositional verification techniques for concurrent and probabilistic systems. Research interests span Programming Languages, Verification, Security, Concurrency Theory, and Probabilistic Programming. Notable contributions include the Soter tool for Erlang verification, the James Bound library for π-calculus analysis, and foundational work on hierarchical communication topologies. His 2016 doctoral thesis on concurrency verification earned the CPHC/BCS Distinguished Dissertation Award. Recent work includes Bluebell (POPL 2025), advancing probabilistic reasoning via relational lifting, and compositional approaches to hypersafety (OOPSLA 2022). Active in academic service, he has delivered keynotes at HYPER’25 and participates in major conferences like CAV and POPL. Awards: 2016 CPHC/BCS Distinguished Dissertation Award Tools: Soter (Erlang verifier), James Bound (π-calculus library) Grants/Advising: Supervises graduate students at University of Konstanz (details forthcoming) Led the Formal Methods research group focused on concurrency, security, and probabilistic systems at the University of Konstanz.

• Computer Networks
• Distributed Systems
• Edge Computing
• +۴ مورد دیگر

Osama Abboud is a researcher affiliated with Technische Universität Darmstadt. He holds a PhD in Computer Science from the same institution (2012), focusing on quality adaptation in peer-to-peer video streaming using scalable video coding. His work spans computer networks, distributed systems, edge computing, and federated learning. Key contributions include innovations in P2P video streaming resilience, serverless federated learning optimization, and mission-critical AI systems. He has collaborated extensively with researchers like Ralf Steinmetz and Michael Gerndt, publishing in venues such as IEEE Trans. Ind. Informatics, NetSoft, and ICC. His research emphasizes practical system design for heterogeneous environments, energy efficiency, and real-time performance. Education: PhD in Computer Science (Darmstadt University of Technology, 2012). Research Interests: Peer-to-Peer Systems, Edge Computing Architecture, Federated Learning Optimization, AI-driven Network Adaptation, and Low-Power Hardware Integration. His work bridges theoretical foundations with practical implementations in distributed environments. Notable Projects: Development of Apodotiko (serverless federated learning framework), MARQ (mission-critical AI system), and contributions to edge computing architectures like Trabant and Host Bypassing. These projects aim to enhance scalability, reduce latency, and improve sustainability in distributed systems.

Gert Jan Hofstedeمشاهده پروفایل

استاد

• Artificial Sociality
• Cultural Evolution
• Agent-Based Modeling
• +۴ مورد دیگر

Prof. Gert Jan Hofstede is a Professor of Artificial Sociality at Wageningen University & Research, affiliated with the Urban Economics Group. His career spans population biology, information systems, and cultural studies. He earned a PhD in production planning and has pioneered agent-based modeling to study human social behavior, emphasizing cultural evolution and complex systems. His research integrates interdisciplinary methods to address societal challenges. Education: Studied at Wageningen University since 1976, with a background in population biology. Research Focus: Cultural psychology, agent-based modeling, intercultural communication, and complex systems. Notable projects include SESAM (agro-forestry management) and SiLiCo (simulating life science complexity). Key Contributions: Authored the 'Artificial Sociality Manifesto' and co-authored Cultures and Organizations: Software of the Mind . His work explores societal dynamics through simulations, emphasizing collective behavior and cultural stability. Awards: Appointed Personal Professor (2018). Advisees: Supervised PhD candidates in interdisciplinary projects. Labs/Teams: Founder of SiLiCo and collaborator in Urban Economics initiatives.

Adina Svenja Wagnerمشاهده پروفایل

پژوهشگر

• Neuroinformatics
• Research Software Engineering
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Adina Svenja Wagner is a researcher at the Institute of Neuroscience and Medicine (INM-7) at the Research Center Jülich GmbH, Germany. Her work focuses on neuroinformatics , research software engineering , and research data management (RDM) . She is a core developer of the DataLad tool, which enables FAIR and reproducible data management in computational neuroscience and beyond. She teaches best practices for open science, reproducibility, and software tools to foster transparent scientific practices. Her roles include coordinating institute activities in AI, RSE, and RDM. Her research interests span open science infrastructure , reproducible workflows , and collaborative data science . Key contributions include advancing DataLad for large-scale datasets, developing frameworks for reproducibility in biomedical research, and promoting inclusive open science practices through initiatives like OHBM Brainhack. Her recent work emphasizes scalable solutions for big data challenges, FAIR principles in neuroscience, and interdisciplinary collaboration. She actively engages in conferences and community-driven projects to advance open science and computational reproducibility.

• Medical Imaging
• Radiomics
• Machine Learning in Healthcare
• +۳ مورد دیگر

Dr. Elisabeth Pfaehler is a Researcher at the Institute of Neuroscience and Medicine (INM-4) at the Research Center Jülich . Her work focuses on advancing PET radiomics , medical imaging algorithms , and machine learning applications in healthcare. She leads efforts in improving diagnostic accuracy and treatment prediction through quantitative imaging biomarkers, particularly in oncology and neurology. Key contributions include: Development of the METhodological RadiomICs Score (METRICS) for radiomics reproducibility. Optimization of PET image reconstruction and segmentation techniques for bulky tumors. Creation of open-source tools like RaCaT (Radiomics Calculator Tool) and SMART PET simulation software. Her research emphasizes interdisciplinary collaboration , with studies involving phantom testing, multicenter trials, and AI-driven image quality control. Current work addresses noise sensitivity in radiomics, uncertainty estimation, and translational applications in clinical settings.